Building Intelligent .NET Applications: Data-Mining Predictions

This chapter uses a fictional retailer named Savings Mart to show how Microsoft's Analysis Services, included with Microsoft SQL Server, can be used to improve operational efficiencies and reduce costs.

This chapter is from the book

Improving human-to-computer interaction through speech processing is just
one area of computing that can benefit from enhanced computing. On the other
side of the interface is the backend, which usually ties in to a database.
It is here that enhanced computing can help users get the most from their data.

Over the past ten years, there has been a dramatic increase in computer usageand
in the number of home users. Electronic commerce has resulted in the collection
of vast amounts of customer and order information. In addition, most businesses
have automated their processes and converted legacy data into electronic formats.
Businesses large and small are now struggling with the question of what to
do with all the electronic data they have collected.

Data warehousing is a multi-billion-dollar industry that involves the collection,
organization, and storage of large amounts of data. Data cubesstructures
comprising one or more tables in a relational databaseare built so that
data can be examined through multiple dimensions. This allows databases containing
millions of records and hundreds of attributes to be explored instantly.

Data mining is the process of extracting meaningful information from large
quantities of data. It involves uncovering patterns in the data and is often
tied to data warehousing because it makes such large amounts of data usable.
Data elements are grouped into distinct categories so that predictions can
be made about other pieces of data. For example, a bank may wish to ascertain
the characteristics that typify customers who pay back loans. Although this
could be done with database queries, the bank would first have to know what
customer attributes to query for. Data mining can be used to identify what
those attributes are and then make predictions about future customer behavior.

Data mining is a technique that has been around for several years. Unfortunately,
many of the original tools and techniques for mining data were complex and
difficult for beginners to grasp. Microsoft and other software makers have
responded by creating easier-to-use data-mining tools. A 2004 report titled "The
Golden Vein" by the Economist.com states:

As the cost of storing data plummets and the power of analytic tools improves,
there is little likelihood that enthusiasm for data mining, in all its forms,
will diminish.

This is the first of two chapters that will examine how a fictional retailer
named Savings Mart was able to utilize Microsoft's Analysis Services,
included with Microsoft SQL Server, to improve operational efficiencies
and reduce costs. The present chapter will examine a standalone Windows program
named LoadSampleData which is used to load values into a database and generate
random purchases for several of the retailer's stores. A mining model
will then be created based on shipments to each store. The mining model will
be the first step toward revising the way Savings Mart procedurally handles
product orders and shipments.

Chapter 6 will extend the predictions made in this chapter through the use
of a Windows service designed to automate mining-model processing and the application
of processing results. Finally, a modified version of the LoadSampleData program
will be used to verify that Savings Mart was able to successfully lower its
operating costs.

The chapter also includes a Microsoft case study which examines a real company
that used Analysis Services to build a data-mining solution. In the case study,
a leaser of technology equipment needed to predict when clients would return
their leased equipment. By using Analysis Services, it was able to quickly
build a data-mining solution that helped to reduce costs and more accurately
predict the value of assets.

Introducing Data Mining with SQL Server

Although SQL Server 7.0 offered Online Analytical Processing (OLAP) as OLAP
Services, it was not until the release of SQL Server 2000 that data-mining
algorithms were included. Analysis Services comes bundled with SQL Server as a
separate install. It allows developers to build complex OLAP cubes and then
utilize two popular data-mining algorithms to process data within the cubes.

Of course, it is not necessary to build OLAP cubes in order to utilize
data-mining techniques. Analysis Services also allows mining models to be built
against one or more tables from a relational database. This is a big departure
from traditional data-mining methodologies. It means that users can access
data-mining predictions without the need for OLAP services.

Data mining involves the gathering of knowledge to facilitate better
decision-making. It is meant to empower organizations to learn from their
experiences—or in this case their historical data—in order to form
proactive and successful business strategies. It does not replace
decision-makers, but instead provides them with a useful and important tool.

The introduction of data-mining algorithms with SQL Server represents an
important step toward making data mining accessible to more companies. The
built-in tools allow users to visually create mining models and then train those
models with historical data from relational databases.

Data-Mining Algorithms

Data mining with Analysis Services is accomplished using one of two popular
mining algorithms—decision trees and clustering. These algorithms are used
to find meaningful patterns in a group of data and then make predictions about
the data. Table 5.1 lists the key terms related to data mining with Analysis
Services.

Table 5.1 Key terms related to data mining with Analysis
Services.

Term

Definition

Case

The data and relationships that represent a single object you wish to
analyze. For example, a product and all its attributes, such as Product Name and
Unit Price. It is not necessarily equivalent to a single row in a relational
table, because attributes can span multiple related tables. The product case
could include all the order detail records for a single product.

Case Set

Collection of related cases. This represents the way the data is viewed and
not necessarily the data itself. One case set involving products could focus on
the product, whereas another may focus on the purchase detail for the same
product.

Clustering

One of two popular algorithms used by Analysis Services to mine data.
Clustering involves the classification of data into distinct groups. As opposed
to the other algorithm, decision trees, clustering does not require an outcome
variable.

Cubes

Multidimensional data structures built from one or more tables in a
relational database. Cubes can be the input for a data-mining model, but with
Analysis Services the input could also be based on an actual relational
table(s).

Decision Trees

One of two popular algorithms used by Analysis Services to mine data.
Decision trees involves the creation of a tree that allows the user to map a
path to a successful outcome.

Testing Dataset

A portion of historical data that can be used to validate the predictions of
a trained mining model. The model will be trained using a training dataset that
is representative of all historical data. By using a testing dataset, the
developer can ensure that the mining model was designed correctly and can be
trusted to make useful predictions.

Training Dataset

A portion of historical data that is representative of all input data. It is
important that the training dataset represent input variables in a way that is
proportional to occurrences in the entire dataset. In the case of Savings Mart,
we would want the training dataset to include all the stores that were open
during the same time period so that no bias is unintentionally introduced.

Decision Trees

Decision trees are useful for predicting exact outcomes.
Applying the decision trees algorithm to a training dataset results in the
formation of a tree that allows the user to map a path to a successful outcome.
At every node along the tree, the user answers a question (or makes a
"decision"), such as "years applicant has been at current job
(0–1, 1–5, > 5 years)."

The decision trees algorithm would be useful for a bank that wants to
ascertain the characteristics of good customers. In this case, the predicted
outcome is whether or not the applicant represents a bad credit risk. The
outcome of a decision tree may be a Yes/No result (applicant is/is not a bad
credit risk) or a list of numeric values, with each value assigned a
probability. We will see the latter form of outcome later in this chapter.

The training dataset consists of the historical data collected from past
loans. Attributes that affect credit risk might include the customer’s
educational level, the number of kids the customer has, or the total household
income. Each split on the tree represents a decision that influences the final
predicted variable. For example, a customer who graduated from high school may
be more likely to pay back the loan. The variable used in the first split is
considered the most significant factor. So if educational level is in the first
split, it is the factor that most influences credit risk.

Clustering

Clustering is different from decision trees in that it
involves grouping data into meaningful clusters with no specific outcome. It
goes through a looped process whereby it reevaluates each cluster against all
the other clusters looking for patterns in the data. This algorithm is useful
when a large database with hundreds of attributes is first evaluated. The
clustering process may uncover a relationship between data items that was never
suspected. In the case of the bank that wants to determine credit risk,
clustering might be used to identify groups of similar customers. It could
reveal that certain customer attributes are more meaningful than originally
thought. The attributes identified in this process could then be used to build a
mining model with decision trees.

OLE DB for Data-Mining Specification

Analysis Services is based on the OLE DB for Data Mining
(OLE DB for DM) specification. OLE DB for DM, an extension of OLE DB, was
developed by the Data Mining Group at Microsoft Research. It includes an
Application Programming Interface (API) that exposes data-mining functionality.
This allows third-party providers to implement their own data-mining algorithms.
These algorithms can then be made available through the Analysis Services
Manager application when building new mining models.